below marketmost similar roles pay hereabove market
This role pays more than
84%
of similar roles. Most pay
$151,000–$235,750
— the blue band above.
At the midpoint, this role pays about
$236k
versus about
$193k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
The Senior System Software Engineer, Interactive World Models joins the FlashDreams team to bridge the gap between research and production systems. You will design, build, and improve production-quality systems for world models, simulation, rendering, and real-time inference. Your daily work involves connecting generated worlds, reconstructed scenes, and controllable simulation states into cohesive end-to-end systems while optimizing performance, quality, and scalability on NVIDIA GPUs. You will collaborate with robotics and autonomous-vehicle teams to integrate capabilities into downstream workflows. The role requires expertise in CUDA, PyTorch, and large-scale ML systems. You must possess experience in areas such as generative video, diffusion models, neural rendering, or Gaussian splatting. This position solves the technical challenge of moving interactive world models from research prototypes into reliable, real-world production environments.
What does a System Software Engineer earn in California?
Median $235750 from 70 postings across 4 companies.
Design, build, and improve production-quality systems for world models, simulation, and rendering.
Develop end-to-end systems for generated worlds, reconstructed scenes, and controllable simulation states.
Optimize performance, quality, scalability, and reliability of systems on NVIDIA GPUs.
Turn technically complex research prototypes into reliable, high-performance products for production.
Integrate new capabilities into downstream workflows for robotics and autonomous vehicle teams.
Own complex modules and cross-team projects that shape technical direction and roadmaps.
Build and optimize GPU-accelerated software using CUDA and modern ML frameworks like PyTorch.
What we're looking for
BS, MS, or PhD in Computer Science, Electrical Engineering, Physics, or a related field, or equivalent experience.
8+ years of relevant software engineering experience.
Experience in world models, generative video, diffusion/flow-matching models, neural rendering, simulation, robotics, autonomous vehicles, computer vision, or large-scale ML systems.
Experience building and optimizing GPU-accelerated software with CUDA and modern ML frameworks such as PyTorch.
Demonstrated engineering judgment in turning complex prototypes into reliable products through testing, delivery, and clear communication.
Hands-on experience building, adapting, evaluating, or deploying world models, video-generation systems, neural simulation, generative 3D, neural rendering, Gaussian splatting, NeRFs, or reconstruction (preferred).
Experience with real-time inference, distributed training, performance profiling, GPU optimization, synthetic-data generation, or digital-twin applications (preferred).
Contributions to open-source ML, graphics, simulation, or developer platforms; publications, patents, or other demonstrated technical leadership (preferred).
World Models
Video Diffusion
Neural Rendering
Gaussian Splatting
NeRFs
Flow-matching
Causal Video Generation
ML Infrastructure
CI/CD
Simulation
Robotics
Autonomous Vehicles
Synthetic Data
Cloud Services
Linux
GPU Systems
CUDA
Distributed Systems
Remote Execution
Profiling
Metrics
Traces
Bazel
Container Infrastructure
High-Performance Computing
Scheduling
NUMA
Hardware-in-the-loop
Data Paths
Caching
Robotics
Embodied AI
System Software
Linux
Embedded Software
Distributed Computing
Real-time Systems
Hardware-in-the-loop
Simulation
Firmware
Device Drivers
Performance Optimization
Sensor Integration
Foundation Models
Reinforcement Learning